Data Preprocessing

Data preprocessing refers to the series of operations performed before training a machine learning or deep learning model, transforming raw data into a format and state suitable for analysis and training. Producing accurate, high-performing models requires improving data quality and eliminating noise and inconsistencies — making data preprocessing a foundational step in AI development. Typical data preprocessing steps include: • Handling missing values: Imputing or removing incomplete data • Outlier detection and removal: Treating anomalies and noise • Normalization and standardization: Adjusting feature scales (e.g., Min-Max scaling, Z-score standardization) • Encoding categorical variables: One-hot encoding, label encoding • Text cleaning: Removing unnecessary symbols, morphological analysis, stopword removal • Shuffling and splitting data: Dividing into training, validation, and test sets • Feature extraction and selection: Extracting key variables and reducing dimensionality Proper data preprocessing directly improves model convergence speed, prevents overfitting, and boosts prediction accuracy. Conversely, poor preprocessing leads to mislearning and degraded performance, requiring careful attention to data characteristics and task requirements. Most major machine learning libraries (scikit-learn, TensorFlow, PyTorch, etc.) provide rich preprocessing tools, greatly improving development efficiency. Data preprocessing is one of the most critical phases of AI development — it is the foundation for creating high-quality data that ultimately determines model success.